Why and how to Use Agentic AI in Software Development Lifecycle

Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI. The reasons are obvious, since agentic AI can autonomously analyze data, generate actions, and execute multi-step tasks without constant human supervision.

With the growing demand for agentic AI, these tools can be used as smart assistants throughout the app development process itself.

In fact, support from agentic AI can increase task completion accuracy by 7.7% across industries.

This is especially relevant to software development, where even minor mistakes can cause disruptions or costly bugs.

In this article, we will discuss the impact of agentic AI on SDLC. We will explore the ways agentic AI supports software engineers and things to consider while using this technology throughout different stages of SDLC.

The term agentic AI encompasses AI-powered systems that can autonomously work toward achieving goals. Agentic AI solutions can independently analyze the environment or inputs, develop autonomous reasoning, take actions on their own, and proactively adapt to changing conditions.

Agentic AI often uses the capabilities of generative AI and LLMs and expands them with additional layers of reasoning and feedback loops. The image below illustrates a typical agentic AI architecture.

With its capabilities for autonomous reasoning, agentic AI can outperform generative AI solutions in many aspects. Unlike generative AI, agentic AI solutions can handle complex, multi-step workflows with little oversight. In some industries, such as banking, agentic AI has already been shown to boost productivity by up to 60%. This technology also has great potential as an assistant throughout the software development lifecycle.

The table below summarizes the key differences between generative and agentic AI.

From Leobits’s experience, agentic autonomous AI systems are especially relevant as assistants in coding or software testing. However, its autonomous reasoning and automation can be relevant across all SDLC stages. Let’s explore how AI agents can assist developers and teams throughout the process.

Agentic AI can assist in collecting and processing information from multiple sources. This ability makes it a useful tool during the requirements gathering and discovery phase. In particular, it can:

• Extract functional and non-functional requirements
• Analyze and validate the feasibility of technical requirements
• Convert various file types into a unified data format for consistency
• Compare key project documents to identify inconsistencies and ambiguities

UI/UX designers can use agentic AI to create initial prototypes and model different user scenarios. In addition, agentic AI can assist solution architects while planning software architecture design. It can plan how components will interact, forecasting scalability or performance issues, and generating architecture diagrams aligned with best practices.

Autonomous coding is one of the most popular use cases for AI tools. In particular, writing new code was the most common use case for artificial intelligence tools in 2024, with 71% of AI users relying on them for coding assistance.

Agentic AI can autonomously generate basic code snippets or code modules. In addition, code editing tools powered by agentic AI, such as Claude Code and Cursor, can provide detailed notes that explain the logic behind specific sections of a codebase. This helps developers quickly analyze and understand how the existing code works. Such a feature is especially valuable for onboarding new software engineers on the project.

Beyond writing code, agentic AI can refactor existing codebases. AI-powered tools can assist software engineers in maintaining consistent coding styles and optimizing workflows through real-time feedback and context-aware suggestions.

AI-assisted coding solutions can also be used for DevOps workflows, such as building and managing CI/CD pipelines. In particular, agentic artificial intelligence can analyze the system’s existing codebase and infrastructure features to provide suggestions for CI/CD development and deployment. Such solutions can also automatically generate YAML or JSON configurations for CI/CD tools like GitHub Actions, GitLab CI, Jenkins, or Azure DevOps. Still, human involvement is required for writing the prompts for AI, as well as monitoring its work and outputs.

Around 62% of users leverage artificial intelligence to write test cases, while 56% use it for code reviews. Agentic AI provides comprehensive support to these and other QA workflows, ensuring automated end-to-end testing.

Some critical capabilities of agentic AI for QA and testing include:

• Continuous monitoring of code changes through integrations with CI/CD pipelines
• Automated, dynamic updates of test suites to adapt to new features
• Identification of high-risk areas of code, which helps QA specialists prioritize test coverage
• Correlation of test results with performance metrics, which enables QA teams to pinpoint the root causes of failures
• Automated generation of detailed bug reports
• Automated suggestions for fixes or refactoring workflows.

By using agentic AI for software testing and QA, specialists can accelerate routine workflows, improve feedback loops, and optimize overall testing efficiency.

Agentic AI strengthens CI/CD pipelines by continuously monitoring code changes, dependencies, and configuration drift across different software versions. Such solutions make integrations with third-party services smoother by detecting integration conflicts and regressions.

Additionally, development teams can use agentic AI to simulate system behavior under varying loads. To minimize system downtime, development teams can configure agentic AI to implement automated rollback when failures or anomalies are detected.

Around 66% of users apply AI for modifying existing code, while over 55% of users leverage it to maintain legacy codebases.

In addition, agentic AI can be applied by support and maintenance teams for system monitoring. Such solutions can detect anomalies and provide smart recommendations for system optimization.

Support teams can also delegate routine workflows like patching or basic performance tuning to agentic AI solutions. With their ability to improve over time, AI agents can soon offer proactive recommendations and support continuous software optimization cycles.

By applying agentic AI in the software development lifecycle, you can achieve measurable improvements in speed, quality, and long-term software efficiency. Here are the main benefits that the use of AI in SDLC can bring you.

The use of agentic AI can increase employee productivity in the technology sector by 45%. In software development, it can automate routine tasks, such as writing repetitive and basic code patterns, writing test cases, and software deployment. In fact, a recent study by the DORA research program found that over 80% of AI tool users reported that artificial intelligence improved their productivity.

By using agentic AI, specialists across industries can enhance the accuracy of task completion by 7.7%. Agentic AI solutions often involve multiple specialized agents that handle distinct yet interconnected tasks like monitoring, test case generation, code review, performance tuning, etc. In fact, over 59% of software engineers using AI solutions report that such tools have positively impacted code quality.

By automating specific workflows in the software development lifecycle, agentic AI can reduce the manual effort required for coding, testing, and maintenance. This, in turn, makes software development projects more cost-effective. As a result, organizations already using agentic AI report cost savings of 30–50% in advanced industries.

When developing a software solution, you can use agentic AI to maintain optimal software performance with minimal supervision, which translates into reduced total cost of ownership (TCO).

Agentic AI systems boost innovation by enhancing experimentation and rapid development of proofs of concepts (PoCs) and prototypes. Software development teams can use their code generation and testing capabilities to quickly build and deploy an early product version, enabling rapid idea validation and functionality testing. For example, we at Leobit used agentic AI tools for suggesting code patterns while developing our PoCs, exploring the capabilities of services such as Azure AI Vision and Azure AI Document Intelligence.

The short answer is no. Agentic AI offers tremendous potential to enhance the software development process. However, such solutions cannot replace human expertise completely. Human judgment and context awareness remain essential for decision-making, handling creative tasks, and ensuring strategic oversight.

In addition, there are concerns regarding the quality of AI outputs. While overall trust in AI is growing, it’s still far from absolute.

Agentic AI solutions may reflect biases in their training data or deliver poor outputs due to data hallucinations. This problem is currently the most significant obstacle to AI adoption, according to 45% of business executives participating in IBM’s study on the ingenuity of generative AI. Such biases can lead to inconsistent recommendations or flawed decision-making throughout different stages of SDLC. Therefore, software engineers should validate the outputs of agentic AI and use feedback loops to minimize such risks.

One of the main limitations of agentic AI systems is that, despite their powerful reasoning capabilities, they often lack human creativity and contextual awareness. Such solutions base their decisions on historical data, which means their creativity is constrained by the patterns present in such data.

Agentic AI typically struggles to produce truly novel solutions or innovative approaches outside of what it has already learned. As a result, human involvement remains essential for making critical architectural or design decisions, especially when creativity or handling edge cases is required in software development.

Around 40% of business executives cite concerns over data privacy and confidentiality as substantial barriers to AI adoption. Advanced agentic AI systems can be enhanced with custom security configurations and advanced workflwos for security monitoring and permission management.

However, to ensure secure operations of such solutions, software engineers should still maintain oversight of permissions and external data dependencies. Human involvement remains crucial for managing access, running in-depth safety audits, enforcing secure development practices, and handling other safety tasks that require contextual judgment and freedom from AI bias.

Agentic AI systems may work inefficiently with legacy software. The point is that older systems often lack modern APIs or standardized documentation. As a result, even the most up-to-date agentic AI tools may struggle to understand dependencies or automate updates. Human oversight becomes vital for guiding the integration process. We also suggest modernizing your software infrastructure before integrating AI systems to prevent compatibility issues and broken system connections that could impact the quality of agentic AI outputs.

As already mentioned, outputs of agentic AI must be evaluated at every SDLC stage. Meanwhile, human involvement is equally essential to maintaining the accuracy and reliability of the same AI system.

To do that, you need to apply continuous tuning, monitoring, and validation practices. Such tasks cannot be fully automated by AI because it can reinforce existing biases, amplify hallucinations, or create feedback loops where models validate their own flawed outputs.

Human involvement is vital for the following workflows:

• Supervising data quality
• Managing feature drift
• Fine-tuning and prompt-tuning of models
• Updating model datasets
• Verifying performance metrics like precision, recall, latency, and resource utilization.

In addition, engineering teams can leverage a deeper understanding of context when troubleshooting issues such as model degradation.

Given these factors, businesses should view agentic AI as a collaborative assistant in software development rather than a replacement for human specialists. Agentic AI can be used to automate repetitive tasks, generate insights, and support brainstorming. Meanwhile, human judgment should remain essential for complex tasks or those requiring creativity.

Around 55% of organizations report a shortage of skilled professionals capable of developing and maintaining agentic AI software. With a global outsourcing market growing at a compound annual rate of 6.51% between 2025 and 2030, outsourcing may become a viable option for companies willing to compensate the shortage of in-house talent and expertise.

Leobit, a team with strong AI development and adoption expertise, is ready to help fill the skill gap. We have significant experience using agentic AI in software engineering during our PoC projects. As a Microsoft Solutions Partner for Digital and App Innovation and a company that has successfully applied several AI agents to support our internal processes, we have a deep understanding of AI technologies and their promises.

If you’re looking to bring your software solution to market quickly with the help of agentic AI, our team is ready to support you. And if you need to develop your own agentic AI tool to improve your development process, Leobit is also ready to step in. And if you struggle to understand the value of implementing artificial intelligence for your business, request agentic AI consulting services from our specialists.

The use of agentic AI in SDLC holds great potential for increased speed, software quality, cost-effectiveness, and innovation. While primarily used in coding and testing, such solutions can support all stages of the software development lifecycle, from planning to continuous support and maintenance. The key point is to view artificial intelligence as an assistant, not a replacement for human specialists, due to AI adoption challenges, such as:

• AI model bias
• Creativity concerns
• Security concerns
• Legacy system integration issues
• Support and maintenance challenges.

To minimize the impact of such issues and leverage the power of agentic AI for software development, rely on Leobit.

Contact us and let’s find out how you can benefit from our strong AI development expertise.